mirror of
https://github.com/open-jarvis/OpenJarvis.git
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Jon Saad-FalconandClaude Opus 4.6
29beeed32d
- Add root CONTRIBUTING.md with incentives (paper acknowledgment, Mac Mini giveaway), contribution tiers, PR process, and maintainership path - Add CODE_OF_CONDUCT.md (Contributor Covenant v2.1) - Add .pre-commit-config.yaml with ruff lint + format hooks - Add GitHub issue templates (bug report, feature request, new eval dataset) - Add PR template with test/lint/format checklist - Rewrite docs roadmap with GitHub Projects structure, current focus areas, and collapsible version history - Remove Development section from MkDocs nav; replace with top-level Roadmap tab - Delete changelog, extending docs (consolidated into CONTRIBUTING.md) - Delete root ROADMAP.md (content now lives in docs site) - Add pre-commit to dev extras in pyproject.toml - Add Roadmap link to README Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
69 lines
4.6 KiB
Markdown
69 lines
4.6 KiB
Markdown
# Roadmap
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OpenJarvis uses **GitHub Projects** boards organized by domain to plan and
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track work. Each board has quarterly columns so contributors can see what's
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coming and where help is needed.
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---
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## How We Plan
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- Work is organized into domain-specific project boards (see below)
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- Each board uses quarterly columns: **Backlog → Q2 2026 → Q3 2026 → Q4 2026 → Future**
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- Issues are labeled by difficulty (`good-first-issue`, `help-wanted`) and type (`type:bug`, `type:feature`, `type:perf`, `type:docs`, `type:eval`)
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- Domain labels (`domain:agents`, `domain:engine`, `domain:tools`, etc.) connect issues to the right board
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Want to contribute? Check the boards below, pick an issue labeled `good-first-issue` or `help-wanted`, and comment **"take"** to claim it.
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---
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## Active Project Boards
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| Board | Scope | Link |
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| **Agents & Tools** | Agent types, tool implementations, MCP | *TBD — boards will be linked once created on GitHub* |
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| **Engine & Inference** | Engine backends, streaming, performance | *TBD* |
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| **Learning & Routing** | GRPO, trace-driven policies, rewards | *TBD* |
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| **Evals & Benchmarks** | Datasets, scorers, benchmark infra | *TBD* |
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| **Frontend & Desktop** | Tauri app, dashboard, leaderboard | *TBD* |
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| **Rust Port** | PyO3 bindings, crate parity | *TBD* |
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---
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## Current Focus Areas
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These are the areas where active development is happening and contributions are most impactful:
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- **GRPO training from trace data** — moving router policies beyond heuristics using reinforcement learning from execution traces
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- **Multi-model orchestration pipelines** — coordinating multiple models within a single query (e.g., small model for classification, large model for generation)
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- **Energy-aware routing** — using power consumption data from telemetry to optimize for energy efficiency alongside latency and quality
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- **Plugin ecosystem** — community-contributed engines, tools, and agents distributed as Python packages
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- **Federated memory** — memory backends that synchronize across devices
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---
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## How to Get Involved
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1. Browse the [project boards](#active-project-boards) for issues that interest you
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2. Look for `good-first-issue` and `help-wanted` labels
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3. Read the [Contributing Guide](../../CONTRIBUTING.md) for the full process
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4. Comment **"take"** on an issue to claim it
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---
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<details>
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<summary><strong>Version History</strong></summary>
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| Version | Phase | Status | Delivers |
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|---|---|---|---|
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| **v0.1** | Phase 0 -- Scaffolding | :material-check-circle:{ .green } Complete | Project scaffolding, registry system (`RegistryBase[T]`), core types (`Message`, `ModelSpec`, `Conversation`, `ToolResult`), configuration loader with hardware detection, Click CLI skeleton |
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| **v0.2** | Phase 1 -- Intelligence + Inference | :material-check-circle:{ .green } Complete | Intelligence primitive (model catalog, heuristic router), inference engines (Ollama, vLLM, llama.cpp), engine discovery and health probing, `jarvis ask` command working end-to-end |
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| **v0.3** | Phase 2 -- Memory | :material-check-circle:{ .green } Complete | Memory backends (SQLite/FTS5, FAISS, ColBERTv2, BM25, Hybrid/RRF), document chunking and ingestion pipeline, context injection with source attribution, `jarvis memory` commands |
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| **v0.4** | Phase 3 -- Agents + Tools + Server | :material-check-circle:{ .green } Complete | Agent system (SimpleAgent, OrchestratorAgent), tool system (Calculator, Think, Retrieval, LLM, FileRead), ToolExecutor dispatch engine, OpenAI-compatible API server (`jarvis serve`) |
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| **v0.5** | Phase 4 -- Learning + Telemetry | :material-check-circle:{ .green } Complete | Learning system (HeuristicRouter policy, TraceDrivenPolicy, GRPO stub), reward functions, telemetry aggregation (per-model/engine stats, export), `--router` CLI flag, `jarvis telemetry` commands |
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| **v1.0** | Phase 5 -- SDK + Production | :material-check-circle:{ .green } Complete | Python SDK (`Jarvis` class, `MemoryHandle`), multi-platform channel system (Telegram, Discord, Slack, WhatsApp, etc.), benchmarking framework (latency, throughput), Docker deployment (CPU + GPU), MkDocs documentation site |
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| **v1.1** | Phase 6 -- Traces + Learning | :material-check-circle:{ .green } Complete | Trace system (`TraceStore`, `TraceCollector`, `TraceAnalyzer`), trace-driven learning, MCP integration layer |
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| **v1.5** | Phase 10 -- Agent Restructuring | :material-check-circle:{ .green } Complete | BaseAgent helpers, ToolUsingAgent intermediate base, NativeReActAgent, NativeOpenHandsAgent, RLMAgent, OpenHandsAgent (SDK), `accepts_tools` introspection, backward-compat shims, CustomAgent removed |
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</details>
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